Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection

Fan, Shu-Kai S.; Su, Chuan‐Jun; Nien, Han-Tang; Tsai, Pei-Fang; Cheng, Chen-Yang

doi:10.1007/s00500-017-2610-y

Cited by 61 publications

(18 citation statements)

References 25 publications

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“…Generally, forecasting models can be categorized into three major categories: classical, artificial intelligence and data-driven [12]. Classical methods are the statistical and mathematical methods, such as Auto-Regressive Integrated Moving Average (ARIMA), Seasonal ARIMA (SARIMA), Naive Bayes, Random Forest, etc.…”

Section: Related Workmentioning

confidence: 99%

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

et al. 2019

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This paper focuses on analytics of an extremely large dataset of smart grid electricity price and load, which is difficult to process with conventional computational models. These data are known as energy big data. The analysis of big data divulges the deeper insights that help experts in the improvement of smart grid’s (SG) operations. Processing and extracting of meaningful information from data is a challenging task. Electricity load and price are the most influential factors in the electricity market. For improving reliability, control and management of electricity market operations, an exact estimate of the day ahead load is a substantial requirement. Energy market trade is based on price. Accurate price forecast enables energy market participants to make effective and most profitable bidding strategies. This paper proposes a deep learning-based model for the forecast of price and demand for big data using Deep Long Short-Term Memory (DLSTM). Due to the adaptive and automatic feature learning mechanism of Deep Neural Network (DNN), the processing of big data is easier with LSTM as compared to the purely data-driven methods. The proposed model was evaluated using well-known real electricity markets’ data. In this study, day and week ahead forecasting experiments were conducted for all months. Forecast performance was assessed using Mean Absolute Error (MAE) and Normalized Root Mean Square Error (NRMSE). The proposed Deep LSTM (DLSTM) method was compared to traditional Artificial Neural Network (ANN) time series forecasting methods, i.e., Nonlinear Autoregressive network with Exogenous variables (NARX) and Extreme Learning Machine (ELM). DLSTM outperformed the compared forecasting methods in terms of accuracy. Experimental results prove the efficiency of the proposed method for electricity price and load forecasting.

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Section: Related Workmentioning

confidence: 99%

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

et al. 2019

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“…Also, it could manage large number of 47 variables as the model inputs. Therefore, these characteristics make the Random Forest model a 1 proper choice for predicting the travel time in this study (Fan et al, 2018). Regarding Figure 3, it can be concluded that both models, especially Random Forest, are 1 well-suited to predict travel time in a short-term horizon (i.e., 15 minutes ahead).…”

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confidence: 88%

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Taghipour¹,

Parsa²,

Mohammadian³

2020

Preprint

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10Having access to accurate travel time is of great importance for both highway network users and 11 traffic engineers. The travel time which is currently reported on several highways is estimated by 12 employing naïve methods and using limited sources of data. This results in unreliable and 13 inaccurate travel time prediction and could impose delay on travelers. Therefore, the main 14 objective of this study is short-term prediction of travel time for highways using multiple data 15 sources including loop detectors, probe vehicles, weather condition, network, accidents, road 16 works, and special events in order to consider the effect of different factors on travel time. To this 17 end, two machine learning methods, K-Nearest Neighbors and Random Forest, are employed. 18 After applying data cleaning process on datasets and combining them, the models are trained to 19 predict and compare short-term harmonic average speed as a representative of travel time for 5-20 minute prediction horizons in one hour ahead. The travel time is calculated as the ratio of the length 21 of each link and the harmonic average speed for all reporting vehicles. Hence, a model is trained 22 for each technique to predict travel time 5 minutes ahead, 10 minutes ahead, and all the way down 23 to 60 minutes ahead. The results confirm satisfying performance of both models in short-term 24 travel time prediction with slightly outperformance of Random Forest model. A feature importance 25 and sensitivity analysis also applied for the Random Forest model, and traffic variables are found 26 as the most effective variables in predicting the travel time. 27 28

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“…When Taiwan constructed the ETC system, support for the collection of traffic data was considered, which resulted in the ETC system being used to collect traffic data. This data is openly published on governmental open data platforms to promote its usage and to enhance its value and quality [20]. Owing to privacy considerations, only highly de-identified versions of raw data on paths taken during each trip (M06A) is released by the governmental open data platforms.…”

Section: Research Materials: Etc Datamentioning

confidence: 99%

Data Re-Identification—A Case of Retrieving Masked Data from Electronic Toll Collection

Huang

Lin

2019

Symmetry

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With the growth of big data and open data in recent years, the importance of data anonymization is increasing. Original data need to be anonymized to prevent personal identification from being revealed before being released to the public. There is a growing variety of de-identification methods which have been proposed to reduce the privacy issues, however, there is still much to be improved. The purpose of this study is to demonstrate the possibilities of re-identification from masked data, and to compare the pros and cons of different de-identification methods. A set of electronic toll collection data from Taiwan was used and we successfully re-identified vehicles with specific patterns. Four de-identification methods were performed and finally we compared the strengths and weaknesses of these methods and evaluated their appropriateness.

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Using machine learning and big data approaches to predict travel time based on historical and real-time data from Taiwan electronic toll collection

Cited by 61 publications

References 25 publications

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities

Application of Machine Learning Techniques in Short-term Travel Time Prediction Using Multiple Data Sources

Data Re-Identification—A Case of Retrieving Masked Data from Electronic Toll Collection

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